使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.

Find a replacement for Llama (R.I.P. 😢)

🦙 Keep using Llama! 🦙

• Llama still works and no other application seems to match Llama in terms of ease of use and cell-tower location (Llama's killer feature)
• Cell-tower location UX seems to be unmatched by the alternatives (training new locations, ignoring towers, seeing location events)

https://c2rsetup.officeapps.live.com/c2r/downloadVS.aspx?sku=community&channel=Stable&version=VS18

https://c2rsetup.officeapps.live.com/c2r/downloadVS.aspx?sku=enterprise&channel=Stable&version=VS18

https://c2rsetup.officeapps.live.com/c2r/downloadVS.aspx?sku=professional&channel=Stable&version=VS18

https://aka.ms/vs/18/Stable/vs_community.exe

Ghostty + Zoxide + Yazi + Oh-My-Zsh 完整配置

• Ghostty - 现代化 GPU 加速终端模拟器
• Zoxide - 智能目录跳转工具（cd 的智能替代）
• Yazi - 快速终端文件管理器
• Oh-My-Zsh - Zsh 配置框架

Current version: 1.0.19 1.0.15 (as of 2018-12-10)

https://www.realtek.com/en/component/zoo/category/network-interface-controllers-10-100-1000m-gigabit-ethernet-usb-3-0-software

http://www.realtek.com.tw/downloads/downloadsView.aspx?Langid=1&PNid=13&PFid=56&Level=5&Conn=4&DownTypeID=3&GetDown=false